change11 position paper

Our increasing reliance on technology has led to an unstoppable demand for new knowledge. Because of our growing requirements for energy, the sector has to continually innovate to extract fuel from more difficult sources. Our need for improved healthcare drives the health sector has to find better forms of diagnosis and care. These demands have led to a growth in ‘immaterial labour’ – work that has knowledge as the output – transforming societal output from the production of material artefacts towards the creation of knowledge (Hardt and Negri, 2003). Our grand challenge is that people have to learn to solve real-world problems faster and more effectively to keep up with demand.

These real-world problems are now too complex to be solved by a single person. The knowledge and expertise needed to solve them is increasingly distributed across networks (Paavola & Hakkarainen, 2005; Nardi, et al 2000). Debates have contested whether large groups of connected people are better able to produce knowledge to solve problems and foster innovation than a select few (Keen, 2005; Surowiecki, 2004). The supposition is that if the problems are open to a large number of people, rather than just a few individuals, the knowledge of ‘the many’ will afford greater diversity and ideas to help solve a given problem.Thus knowledge building may be more effective when large numbers of individuals draw upon and, at the same time, feed into the collective knowledge – the knowledge distributed across people, machines, networks and artefacts. Through connecting and making sense of knowledge fragments within the large pool of collective knowledge, people are learning (Siemens, 2005), How people learn by navigating the collective knowledge is yet unknown.

‘Collective learning’ is the term used to describe learning processes that make use of this collective knowledge (see for example Stankeveciute & Jucivicius). A unique aspect of collective learning is it generates a new paradigm for learning in which the individual and ‘the many’ are indivisible, in the same way as an individual user of a social network is inseparable from the set of connections that comprises the network itself. Traditionally, learning has been viewed as either cognitive (individualistic) or social (participatory) (Sfard, 1998). This third metaphor of learning through social knowledge creation breaks from the dichotomy of learning as individual knowledge acquisition or as participation in social practice (Paavola, & Hakkarainen, 2005; Paavola, Lipponen & Hakkarainen, 2004). Individual people learn by both drawing on and, at the same time, contributing to collective knowledge. This knowledge-creation approach to learning highlights those kinds of activities where people collaboratively develop new knowledge artefacts and products while working and learning. It is inherently linked with ‘immaterial labour’. We need a better understanding of the interrelationships between the individual and the collective by collating and analysing examples of learning that bind the individual and the collective. This will be our first quest.

Collective learning is framed by a number of societal and technological trends:

Firstly, knowledge is becoming increasingly openly available for problem solving and learning. To solve complex problems and to learn people have to find ways connecting more and more openly available knowledge across the collective knowledge space to create new meanings (Jonassenn and Land, 2000). Characteristics of collective learning include connections across people, teams, organizations, communities, and societies as well as the relationships, shared vision and meanings generated from the wealth of available knowledge (Ganavan and McArthy, 2008). For learning to be effective, these connections and relationships have to help individual people in navigating and making sense of the collective knowledge (Margaryan,Milligan & Littlejohn, 2009). Social technologies (Web2.0) are a potentially effective way to connect people who create knowledge together, working in networks, situated within the collective knowledge space (Littlejohn, Margaryan &Milligan, 2009).

Jon Dron’s empirical research around sensemaking and the ‘collective’ conscious (Dron, 2003) demonstrated how social software provides an extra dimension to learning, in addition to conventional interactions between learners, teachers and knowledge resources (Dron, 2004). Learners co-operate within different constructs, such as groups, networks and with the collective (Dron and Anderson, 2009). Their cooperation is dependent on processes of discovery, synthesis and sharing of fragmented (tacit and explicit) knowledge. As they build knowledge, the knowledge changes and diversifies (Kaschig et al, 2010). However, we don’t have a good understanding of the ‘binding force’ that connects people while they are learning and building knowledge. This will be our second quest.

One suggestion, from socio-cultural theory, is that people connect via so-called ‘social objects’ (Knorr-Cetina, 2001). For example, health professionals working on a common case will bring knowledge together from different disciplinary domains into a single case report (Edwards, 2010). The case report is the social object that connects health professionals who are working together. The social object connecting people who are learning together could be a shared learning goal that binds individuals as they journey together through the collective knowledge to achieve their goal (Littlejohn, Margaryan & Milligan, 2009). Our early research suggests that a ‘learning goal’ could be the social object that binds people together to solve problems. Individual people might connect with others by sharing a common learning goal. While achieving their goal they can journey together, navigating and make sense of the collective knowledge – a sensemaking process we have termed ‘charting’ (ibid). Understanding the relationship between the individual and the collective and the implications of their association for learning and knowledge building is fundamental to appreciating how social technology tools can impact learning.

Secondly, our view of what constitutes learning is broadening as the knowledge-creation view of learning questions and challenges the conventional controls and boundaries around learning (Paavola, Lipponen & Hakkarainen, 2004). Changes in the way learners work together (in groups, networks and collectives) to build knowledge is mirrored by a shift in conceptual debates about what constitutes learning at work. The view of learning in the workplace has moved from individual problem solving (Schmidt, Norman & Boshuizen, 1990) to knowledge building negotiated with others around tasks (Paavola and Hakkarainen, 2005; Engeström & Middleton, 1996). We don’t have a clear picture of how knowledge workers learn and how collective learning can improve learning and development in the workplace. This will be our third quest.

Our research with large organisations is improving our understanding about what knowledge workers do as they carry out their work and learning goals and make sense of the available knowledge (Margaryan, Milligan & Littlejohn 2011). We know that, while working within the collective knowledge space, individuals carry out discreet, yet intertwined, actions of connecting,consuming, creating and contributing knowledge (Margaryan, Milligan & Littlejohn, 2009). Our work is important in solving the problem many organisations experience in trying to make sure novice workers develop expertise as quickly as possible. The time lag between beginning a new job and being able to work ‘competently’ is termed ‘time to competence’. This time lag can be up to five years in some industries (eg graduate engineers in the energy sector).

Insight into how novices can develop expertise while drawing on the collective knowledge comes from research on how knowledge workers learn across sites. We know that expertise development involves interpreting a common problem, then finding appropriate responses to those interpretations (Edwards, 2010). Expertise development is, therefore, best situated within continuous workplace learning, where people work on real-world, common problems, rather than being contained within formal training (Eraut, 2007; Billett, 2002).

Thirdly, new knowledge practices connecting people and knowledge are emerging. As different types of knowledge resources come on-stream (Littlejohn, Falconer & McGill, 2008; Margaryan and Littlejohn, 2008; Falconer & Littlejohn, 2007), learners are unsure as to how they can use these resources for learning (Littlejohn and Margaryan 2010; Littlejohn and Margaryan 2006). One of the factors that distinguishes an expert from a novice (who has a much simpler concept map of the collective knowledge space) is the ability to navigate knowledge as a holistic network with multiple links (Bradley, Paul and Seeman, 2006). Becoming competent could be viewed as the ability to perceive the links between these loosely related knowledge fragments (Falconer, 2008; Siemens, 2005).

Our research in learner literacies calls for new literacy practices that enables learners to navigate and use the collective knowledge space in ways that develops their competence (Beetham, McGill & Littlejohn, 2008). Our research exposes an immense – and growing – gap between knowledge practices in higher education and in workplaces raising questions around what constitutes ‘literacies’ and how these might be integrated within the curriculum to ensure learners are better prepared for the workplace (Beetham, Littlejohn & Milligan, 2011). Perhaps more importantly, if learning is to become more self-regulated, rather than teacher-regulated, what sorts of mindsets do learners require to take control and self regulate their own learning (Zimmermann & Schunk, 2001).In task 4 we will explore the practices, literacies and mindsets do people need for collective learning.

Fourthly, we increasingly rely on networked technologies, making choices and behaviours explicit, instantly recorded and potentially analysed to make sense of collective knowledge. Learning analytics is already examining how people solve problems, recording learner preferences and using predictive analytics to offer personalisation and adaptation of learner connections. The learning pathways and behaviours of previous learners are potentially become a valuable resource that future learners can source and use. Complex problem solving requires learners to have flexible responses which could not have been anticipated (Nardi, Whittaker and Schwarz, 2000). But we don’t yet understand how different resource types might support collective learning and knowledge building. This will be our fifth quest.

As researchers, we need to fully understand collective learning processes, the factors that affect these, and the emergent nature of collective learning. As practitioners, we have to face the challenges around whether collective learning can be planned, structured, and managed. As learners, we have to understand the inter-relationships between individual and the collective.

The aim of this week is to introduce ideas around collective learning. During the week we will collectively explore what is ‘the individual’ what is ‘the collective’ and examine how technology helps us redefine relationships between the two. I hope you will join our collective quests.